Kotlin for Machine Learning: Opportunities and Limitations in Practice

Kotlin for Machine Learning: Opportunities and Limitations in Practice

The world of machine learning (ML) is firmly associated with Python: TensorFlow, PyTorch, scikit-learn are the de facto standard. However, Kotlin, a modern language for the JVM, is gaining popularity in backend development and Android. A natural question arises: can Kotlin be effectively used for ML? In this article, we will examine the real capabilities, libraries, and limitations of Kotlin in the context of machine learning, backing up the theory with practical code examples.

Why Kotlin, not Python? Key Advantages

Python remains the king of ML thanks to its vast community and libraries. But Kotlin has its own trumps that make it attractive for production systems:

• Static typing and null safety. Type errors and NullPointerException are caught at compile time, which is critical for reliable ML pipelines.
• JVM performance. Kotlin code compiles to JVM bytecode, which often runs faster than interpreted Python (especially in loops and when working with large data volumes).
• Interoperability with Java. You can use any Java library for ML (Deeplearning4j, Smile, Apache Spark MLlib) directly from Kotlin without wrappers.
• Conciseness and functional capabilities. Data classes, lambdas, extension functions make code cleaner and more expressive.

Key Libraries for ML in Kotlin

Although Kotlin cannot boast the same variety of libraries as Python, mature solutions already exist for practical tasks.

1. KotlinDL (library from JetBrains)

This is a native deep learning library for Kotlin, built on the TensorFlow Java API and ONNX Runtime. It provides a high-level API for creating and training neural networks.

// Example: a simple neural network for classification using KotlinDLimport org.jetbrains.kotlinx.dl.api.core.Sequentialimport org.jetbrains.kotlinx.dl.api.core.layer.core.Denseimport org.jetbrains.kotlinx.dl.api.core.layer.activation.Reluimport org.jetbrains.kotlinx.dl.api.core.layer.activation.Softmaximport org.jetbrains.kotlinx.dl.api.core.optimizer.Adamimport org.jetbrains.kotlinx.dl.dataset.iris.IrisDataset

fun main() {    // Loading the Iris dataset    val (train, test) = IrisDataset.load()

    // Defining the model    val model = Sequential.of(        Dense(10, activation = Relu),        Dense(3, activation = Softmax) // 3 iris classes    )

    // Compilation and training    model.use {        it.compile(optimizer = Adam(), loss = "categorical_crossentropy")        it.fit(dataset = train, epochs = 50, batchSize = 16)

        // Evaluation        val accuracy = it.evaluate(dataset = test).metrics["accuracy"]        println("Accuracy on test data: $accuracy")    }}

2. Smile (Statistical Machine Intelligence & Learning Engine)

Smile is a powerful Java library that works great from Kotlin. It covers classic ML methods: SVM, Random Forest, K-means, PCA, NLP, and much more.

// Example: K-Means using Smile + Kotlinimport smile.clustering.KMeansimport smile.data.`type`.DataTypesimport smile.data.`type`.StructTypeimport smile.data.vector.DoubleVectorimport smile.io.Read

fun main() {    // Loading data (example - CSV)    val schema = StructType(        DoubleVector.of("feature1", DataTypes.DoubleType),        DoubleVector.of("feature2", DataTypes.DoubleType)    )    val data = Read.csv("data/sample.csv", delimiter = ',', header = true, schema = schema)

    // Conversion to matrix    val matrix = data.toMatrix()

    // Clustering into 3 clusters    val kmeans = KMeans.fit(matrix, 3, 100)

    // Output centroids    println("Cluster centroids:")